This PR consolidates both #21667 and #21759 (look there for features), but improves on them in the following way:
- [x] we reverted renaming of existing projects `tune`, `rllib`, `train`, `cluster`, `serve`, `raysgd` and `data` so that links won't break. I think my consolidation efforts with the `ray-` prefix were a little overeager in that regard. It's better like this. Only the creation of `ray-core` was a necessity, and some files moved into the `rllib` folder, so that should be relatively benign.
- [x] Additionally, we added Algolia `docsearch`, screenshot below. This is _much_ better than our current search. Caveat: there's a sphinx dependency that needs to be replaced (`sphinx-tabs`) by another, newer one (`sphinx-panels`), as the former prevents loading of the `algolia.js` library. Will follow-up in the next PR (hoping this one doesn't get re-re-re-re-reverted).
This fixes the previous problems from team column revert.
This has 2 additional changes;
alert handler receives the team argument, which was the root cause of breakage; https://github.com/ray-project/ray/pull/21289
Previously, tests without a team column were raising an exception, but I made the condition weaker (warning logs). I will eventually change it to raise an exception, but for smoother transition, we will log warning instead for a short time
Please review **e2e.py and test_suite belonging to your team**!
This is the first part of https://docs.google.com/document/d/16IrwerYi2oJugnRf5hvzukgpJ6FAVEpB6stH_CiNMjY/edit#
This PR adds a team name to each test suite.
If the name is not specified, it will be reported as unspecified.
If you are running a local test, and if the new test suite doesn't have a team name specified, it will raise an exception (in this way, we can avoid missing team names in the future).
Note that we will aggregate all of test config into a single file, nightly_test.yaml.
Quotation marks were needed in Anyscale app configs to avoid install errors when # were used e.g. in URLs.
Since this has been fixed on the Anyscale side, we can get rid of these.
* use nightly
* switch ml cpu to ray cpu
* fix
* add pytest
* add more pytest
* add constraint
* add tensorflow
* fix merge conflict
* add tblib
* fix
* add back uninstall
* [release] update torch_tune_serve_test to use anyscale connect
* use download_results to download model checkpoint
* clean up code to support both OSS and Anyscale
